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Pancreatic Ductal Adenocarcinoma (PDAC): a review of recent advancements enabled by artificial intelligence
Simple Summary Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the deadliest
forms of cancer, characterized by high rates of metastasis, late detection, and poor …
forms of cancer, characterized by high rates of metastasis, late detection, and poor …
[PDF][PDF] When federated learning meets medical image analysis: A systematic review with challenges and solutions
Deep learning has been a powerful tool for medical image analysis, but large amount of
high-quality labeled datasets are generally required to train deep learning models with …
high-quality labeled datasets are generally required to train deep learning models with …
A survey on heterogeneity taxonomy, security and privacy preservation in the integration of IoT, wireless sensor networks and federated learning
Federated learning (FL) is a machine learning (ML) technique that enables collaborative
model training without sharing raw data, making it ideal for Internet of Things (IoT) …
model training without sharing raw data, making it ideal for Internet of Things (IoT) …
A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …
Survey of federated learning models for spatial-temporal mobility applications
Federated learning involves training statistical models over edge devices such as mobile
phones such that the training data are kept local. Federated Learning (FL) can serve as an …
phones such that the training data are kept local. Federated Learning (FL) can serve as an …
Attentive modeling and distillation for out-of-distribution generalization of federated learning
Out-of-distribution issues lead to different optimization directions between clients, which
weakens collaborative modeling in federated learning. Existing methods aims to decouple …
weakens collaborative modeling in federated learning. Existing methods aims to decouple …
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
The generation of Synthetic Computed Tomography (sCT) images has become a pivotal
methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) …
methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) …
A multifaceted survey on federated learning: Fundamentals, paradigm shifts, practical issues, recent developments, partnerships, trade-offs, trustworthiness, and ways …
Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …
environments because it does not require data to be aggregated in some central place to …
Mapseg: Unified unsupervised domain adaptation for heterogeneous medical image segmentation based on 3d masked autoencoding and pseudo-labeling
Robust segmentation is critical for deriving quantitative measures from large-scale multi-
center and longitudinal medical scans. Manually annotating medical scans however is …
center and longitudinal medical scans. Manually annotating medical scans however is …
A review of the Segment Anything Model (SAM) for medical image analysis: Accomplishments and perspectives
The purpose of this paper is to provide an overview of the developments that have occurred
in the Segment Anything Model (SAM) within the medical image segmentation category over …
in the Segment Anything Model (SAM) within the medical image segmentation category over …